Mapa De Procesos

Oil is a necessary component in an aero engine, partly as a lubricant to reduce the friction between the countless rotating parts, but also as a coolant, as it reduces the temperatures caused by such friction. Observation suggests that changes in the properties of the oil, such as consumption, or the appearance of combustion derivatives, could result in a malfunction in the parts with which it comes into contact. Hence, several methods have been developed for providing engine diagnostics by examining the oil within them. It is been proved that condition monitoring of the oil is a very reliable primary technique for engine diagnostics, and for this reason it is assigned to the engine diagnostic methods and is more accurate than the mechanical ones.

The main focus of oil monitoring is to detect any changes in the oil’s physical and chemical properties, or to detect particles within it. The results of an oil analysis, or preferably the combination of that analysis with other monitoring methods, could produce accurate and reliable indications of the condition of the component, and hence could constitute a significant enhancement to the predictive maintenance process.

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Oil analysis-based diagnostics share two great advantages, namely low cost and lack of complexity in being performed. However the core techniques of oil analysis give reliable but basic, unsophisticated results, and so it is necessary to implement more sophisticated technologies in order to achieve the diagnostic goal. However, depending on the time window to which the analysis applies, oil analysis is separated into on-line and off-line processes, which generally refers to the processes that can be performed on the aircraft (on- line) and those which have to be performed on the ground (off-line), but which concern the same component.

The on-line process refers to those methods that are capable of providing results at the exact time of measuring and sampling, and requires constant monitoring by the system. Modern systems use the oil debris detection method in order to provide instant indications of faults. However, systems that indicate the oil temperature or oil consumption and are hosted as standard on an aircraft could also be classified as on-line process systems, even if they don’t have a direct diagnostic purpose (Hörl and Richter, 1995). Conversely, the off-line process is characterized by the convenience of time, and mostly applies to methods that require laboratory chemical analysis. Moreover, it is possible to perform more detailed analysis that requires past and present record data for prognostics reasons.

Different techniques have been applied over the years for oil analysis, each focusing on a different aspect, with the fidelity of the results being affected positively by the combination of methods. However, it is clear that these oil analysis methods can be categorized into two general groups. The first group refers to those techniques that focus on finding any physical or chemical changes within the oil itself. This group is known as Oil Condition Monitoring, and includes the three most commonly found analysis techniques.

The first and simplest technique is the Blotting Paper Test. This is a simple visual method which uses a special paper which takes on different colours depending on the clarity and the condition of the oil. Next is the Capacitance Test, a method that is used in order to determine the contamination of the oil resulting from static electric charges, the presence of water, methane debris and acidic oxidization. The presence of any of these will result in a reduction in the oil’s capacitance, and so provides an indication of an incipient failure. The final method is the Viscosity Test, which is carried out in the laboratory. This test usually focuses on fuel contamination, which is indicated by a decrease in viscosity, which may mean fuel dilution (Li, 2008).

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The second group of methods is called Oil Debris Monitoring. The purpose of such techniques is the detection of contaminant debris found in the oil. While such debris could take the form of solid particles, or else could be liquid or gaseous, this method focuses primarily on solid particles because they could be an indication of wear on engine parts, especially on bearings. The amount of the debris found provides indications of the condition of the bearings, and most importantly for this case, whether they are degraded. It should be noted that not all solid particles are the outcome of the process of wear, as some could be ingested from the atmosphere. Additional attention should be paid to the time at which such debris is produced, because there is an acceptable amount of wear for all parts when they are new and have just been installed (Miller and Kitaljevich, 2000).

For this reason modern oil debris monitoring systems combine the results of the analysis with model-based knowledge which is predefined cases in a model form in order to provide assessments of the remaining useful life of the component they refer to (Orsagh et al., 2003). Because debris monitoring focuses on the debris resulting from bearing wear, and because the engine bearings are considered to be flight critical parts, a significant amount of development has gone into these techniques and they can now be found on many engine applications as an on-line process system. The most common methods for detecting debris are ferrography, the magnetic chip detector or magnetic plugs, and analysis of the filter for debris by using microscopes (Rao, 1996).

One state-of-the-art oil monitoring technique is being developed under the JSF PHM program. With this technique, the engine is equipped with a system ready to provide on-line information about the condition of the engine by exploiting the advantages of oil condition monitoring in combination with artificial intelligence (Powrie and Fisher, 1999). This is achieved by monitoring the electrostatic charge carried by the debris in the oil. Even though these systems are in the earliest stages of development, they will provide a significant enhancement by overcoming deficiencies such as the difficulty of detecting non-metallic debris (Tasbaz et al., 1999).

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